In general, autonomous vehicles have used control system architectures to perform autonomous driving functions to travel a trajectory plan having an origin and a destination. For autonomous operation, active environment scanning has been required to assess conditions local to a vehicle, and to then produce an action to progress towards an autonomous objective (such as increasing speed, coming to a stop at a stop light, etc.). Also, because of the variety of vehicles (such as sports vehicles, suburban utility vehicles, outdoor vehicles, pickup trucks, etc.), the high sampling and scanning rates for assessing these environments generate large raw data volumes to process and make sense of within a limited time, which generally overburdens the sensor devices and processing devices, frustrating the ability to make effective and timely autonomous decisions. A need exists for a method and device that can optimize autonomous vehicle decision-making in a vehicle environment so as to form timely action decisions (such as whether to accelerate, decelerate, etc.) while also taking into account the varying and largely diverging operational models of the variety of non-similar (or heterogeneous) autonomous-capable vehicles.